In [124]:
#Librairies 
import pandas as pd
import missingno as msno
from pandas.api.types import is_numeric_dtype
import os

from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.cluster import KMeans
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
from sklearn import tree

import matplotlib.pyplot as plt
import plotly.express as px
import seaborn as sns


import warnings
warnings.filterwarnings("ignore")

%matplotlib inline
In [5]:
#Load file
patient_excel = pd.ExcelFile("patients.xlsx")
df =patient_excel.parse("patient")
In [6]:
df.head(5)
Out[6]:
Patient Nb State Patient Nb Date Announced Estimated Onset Date Age Bracket G Detected City Detected District Detected State State code Current Status Notes Contracted from which Patient (Suspected) Nationality Type of trans Status Change Date Backup Notes Num Cases Entry_ID
0 1.0 KL-TS-P1 30/01/2020 2020-01-26 00:00:00 20 F Thrissur Thrissur Kerala KL Recovered Travelled from Wuhan NaN India Imported 14/02/2020 Student from Wuhan 1.0 NaN
1 2.0 KL-AL-P1 02/02/2020 29/01/2020 NaN NaN Alappuzha Alappuzha Kerala KL Recovered Travelled from Wuhan NaN India Imported 14/02/2020 Student from Wuhan 1.0 NaN
2 3.0 KL-KS-P1 03/02/2020 NaN NaN NaN Kasaragod Kasaragod Kerala KL Recovered Travelled from Wuhan NaN India Imported 14/02/2020 Student from Wuhan 1.0 NaN
3 4.0 DL-P1 02/03/2020 NaN 45 M East Delhi (Mayur Vihar) East Delhi Delhi DL Recovered Travelled from Austria, Italy NaN India Imported 15/03/2020 Travel history to Italy and Austria 1.0 NaN
4 5.0 TS-P1 02/03/2020 NaN 24 M Hyderabad Hyderabad Telangana TG Recovered Travelled from Dubai to Bangalore on 20th Feb,... NaN India Imported 02/03/2020 Travel history to Dubai, Singapore contact 1.0 NaN
In [7]:
#Filter columns
target_columns = [
    "Age Bracket","G","Detected District","Detected State","Current Status"
]
df = df[df.columns[df.columns.isin(target_columns)]]
In [8]:
msno.matrix(df)
Out[8]:
<matplotlib.axes._subplots.AxesSubplot at 0x2839e114e20>
In [9]:
#Drop missing data
df.dropna(axis=0, how='any', inplace=True)
msno.matrix(df)
Out[9]:
<matplotlib.axes._subplots.AxesSubplot at 0x2839c0e35b0>
In [15]:
#correlation = df.corr()
#plt.figure(figsize=(20,10))
#sns.heatmap(correlation, vmax=1, square=True,annot=False,cmap='cubehelix')
#
#plt.title('Correlation between different fearures')
In [16]:
for col in df.columns:
    df = df.astype({col: str}, errors='raise')
In [17]:
#Encoding Labels (because type is object)
le = LabelEncoder()
for var in target_columns:
      df[var+str("_enc")] = le.fit_transform(df[var])
In [18]:
filter_col = [col for col in df if col.endswith('_enc')]
df_kmeans = df[df.columns[df.columns.isin(filter_col)]]
In [19]:
#The elbow method
cs = []
for i in range(1, 11):
    kmeans = KMeans(n_clusters = i, init = 'k-means++', max_iter = 300, n_init = 10, random_state = 0)
    kmeans.fit(df_kmeans)
    cs.append(kmeans.inertia_)
plt.plot(range(1, 11), cs)
plt.title('The Elbow Method')
plt.xlabel('Number of clusters')
plt.ylabel('CS')
plt.show()
In [20]:
#k-means algorithme
kmeans = KMeans(n_clusters=4,random_state=0)

kmeans.fit(df_kmeans)

labels = kmeans.labels_
In [21]:
result = pd.DataFrame(labels , index=df["Detected State"], columns=["group"])
result["State"]  = result.index
result.reset_index(drop=True, inplace=True)
In [22]:
result.sample(5)
Out[22]:
group State
91165 1 Karnataka
3843 0 Tamil Nadu
87753 3 Karnataka
37162 3 Bihar
54031 1 Karnataka
In [23]:
#Visualization
fig = px.choropleth(
    result,
    geojson="https://gist.githubusercontent.com/jbrobst/56c13bbbf9d97d187fea01ca62ea5112/raw/e388c4cae20aa53cb5090210a42ebb9b765c0a36/india_states.geojson",
    featureidkey='properties.ST_NM',
    locations='State',
    color='group',
    color_continuous_scale='Reds'
)

fig.show()
In [24]:
decisionTree_col = ["G_enc","Age Bracket_enc","Current Status_enc","Detected State_enc"]
df_DecisionTree = df[df.columns[df.columns.isin(decisionTree_col)]]
In [25]:
X = df_DecisionTree.loc[:, df_DecisionTree.columns != 'Current Status_enc']
y = df_DecisionTree["Current Status_enc"]
In [26]:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
model = DecisionTreeClassifier(random_state=42)
model = model.fit(X_train, y_train)
In [27]:
y_pred = model.predict(X_test)
print("\033[1m Accuracy of Decision Tree Classifier on test set:","{:.2%}".format(accuracy_score(y_test, y_pred)))

 Accuracy of Decision Tree Classifier on test set: 96.14%
In [28]:
fig = plt.figure(figsize=(25,20))
tree.plot_tree(model,filled=True)
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LDA

In [178]:
test_xls = pd.ExcelFile("tests_state_wise.xlsx")
test_df = test_xls.parse("Worksheet")
In [179]:
test_df.drop_duplicates(subset=['State'], inplace=True)
msno.matrix(test_df)
Out[179]:
<matplotlib.axes._subplots.AxesSubplot at 0x283b54c7eb0>
In [180]:
test_df.drop(columns=test_df.columns[7:], inplace=True)
test_df.drop(columns=["date "], inplace=True)
test_df = test_df.dropna(axis=0)
test_df.set_index(keys="State", inplace=True)
msno.matrix(test_df)
Out[180]:
<matplotlib.axes._subplots.AxesSubplot at 0x283abcf8190>
In [181]:
key = [1,2,3,4]
enc_test = dict(zip(test_df["Tag (Total Tested)"].unique(),key))
In [182]:
enc_labels = []
for idx , val in enumerate(test_df["Tag (Total Tested)"].iteritems()):
    enc_label = enc_test[val[1]]
    enc_labels.append(enc_label)
test_df["Tag_enc"] = enc_labels
test_df.drop(columns=["Tag (Total Tested)"], inplace=True)
test_df.reset_index()
Out[182]:
State Total Tested Positive Negative Unconfirmed Tag_enc
0 Andaman and Nicobar Islands 1403.0 12.0 1210 181.0 1
1 Andhra Pradesh 1800.0 132.0 1175 493.0 2
2 Arunachal Pradesh 206.0 1.0 185 20.0 3
3 Bihar 3037.0 32.0 2299 706.0 2
4 Chandigarh 124.0 18.0 98 8.0 2
5 Chhattisgarh 1232.0 9.0 921 302.0 2
6 Goa 220.0 5.0 197 18.0 1
7 Gujarat 4224.0 186.0 3905 133.0 2
8 Haryana 1325.0 44.0 938 343.0 1
9 Jammu and Kashmir 1551.0 106.0 1429 16.0 4
10 Karnataka 4587.0 128.0 4281 178.0 2
11 Kerala 7965.0 265.0 7256 444.0 1
12 Madhya Pradesh 2812.0 193.0 1954 665.0 3
13 Meghalaya 552.0 7.0 299 246.0 2
14 Mizoram 58.0 1.0 0 0.0 3
15 Nagaland 60.0 0.0 47 13.0 1
16 Odisha 1395.0 20.0 1108 267.0 2
17 Puducherry 1207.0 7.0 1159 41.0 1
18 Rajasthan 12279.0 260.0 11439 580.0 4
19 Tamil Nadu 3684.0 411.0 2789 484.0 1
20 Telangana 14962.0 858.0 14104 768.0 3
21 Uttar Pradesh 5255.0 278.0 4796 181.0 1
22 Uttarakhand 678.0 7.0 554 117.0 1
23 West Bengal 659.0 37.0 568 54.0 2
In [183]:
test_df_X = test_df.loc[:, test_df.columns != "Positive"]
test_df_y = test_df["Positive"]
In [184]:
X_train, X_test, y_train, y_test = train_test_split(test_df_X, test_df_y, test_size=0.33, random_state=42)
clf = LinearDiscriminantAnalysis(n_components=3)
clf.fit(X_train,y_train)
Out[184]:
LinearDiscriminantAnalysis(n_components=3)
In [185]:
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
import seaborn as sns
import numpy as np

fig, ax = plt.subplots(1, 1, figsize=(12, 10))

sns.heatmap(pd.DataFrame(clf.coef_, 
                         columns=X_train.columns , 
                         index=[clf.classes_]), 
            ax=ax, cmap='RdBu', annot=True)

plt.suptitle('LDA Feature Coefficients')
pass
In [187]:
X_hat = clf.fit_transform(X, y)

import matplotlib as mpl

colors = mpl.cm.get_cmap(name='tab20').colors
categories = pd.Categorical(pd.Series(y)).categories
ret = pd.DataFrame(
    {'C1': X_hat[:, 0], 'C2': X_hat[:, 1], 'Type': pd.Categorical(pd.Series(y))}
)

fig, ax = plt.subplots(1, figsize=(12, 6))

for col, cat in zip(colors, categories):
    (ret
         .query('Type == @cat')
         .plot.scatter(x='C1', y='C2', color=col, label=cat, ax=ax,
                       s=100, edgecolor='black', linewidth=1,
                       title='Two-Component LDA Decomposition')
         .legend(bbox_to_anchor=(1.2, 1))
    )
In [ ]: